Understanding Programs by Exploiting (Fuzzing) Test Cases

Semantic understanding of programs has attracted great attention in the community. Inspired by recent successes of large language models (LLMs) in natural language understanding, tremendous progress has been made by treating programming language as another sort of natural language and training LLMs on corpora of program code. However, programs are essentially different from texts after all, in a sense that they are normally heavily structured and syntax-strict. In particular, programs and their basic units (i.e., functions and subroutines) are designed to demonstrate a variety of behaviors and/or provide possible outputs, given different inputs. The relationship between inputs and possible outputs/behaviors represents the functions/subroutines and profiles the program as a whole. Therefore, we propose to incorporate such a relationship into learning, for achieving a deeper semantic understanding of programs. To obtain inputs that are representative enough to trigger the execution of most part of the code, we resort to fuzz testing and propose fuzz tuning to boost the performance of program understanding and code representation learning, given a pre-trained LLM. The effectiveness of the proposed method is verified on two program understanding tasks including code clone detection and code classification, and it outperforms current state-of-the-arts by large margins. Code is available at https://github.com/rabbitjy/FuzzTuning.Comment: Findings of the Association for Computational Linguistics: ACL 202

Decomposed Mutual Information Optimization for Generalized Context in Meta-Reinforcement Learning

Adapting to the changes in transition dynamics is essential in robotic applications. By learning a conditional policy with a compact context, context-aware meta-reinforcement learning provides a flexible way to adjust behavior according to dynamics changes. However, in real-world applications, the agent may encounter complex dynamics changes. Multiple confounders can influence the transition dynamics, making it challenging to infer accurate context for decision-making. This paper addresses such a challenge by Decomposed Mutual INformation Optimization (DOMINO) for context learning, which explicitly learns a disentangled context to maximize the mutual information between the context and historical trajectories, while minimizing the state transition prediction error. Our theoretical analysis shows that DOMINO can overcome the underestimation of the mutual information caused by multi-confounded challenges via learning disentangled context and reduce the demand for the number of samples collected in various environments. Extensive experiments show that the context learned by DOMINO benefits both model-based and model-free reinforcement learning algorithms for dynamics generalization in terms of sample efficiency and performance in unseen environments.Comment: NeurIPS 202

Self-reductive synthesis of MXene/Na0.55Mn1.4Ti0.6O4 hybrids for high-performance symmetric lithium ion batteries.

Increasing environmental problems and energy challenges have created an urgent demand for the development of green and efficient energy-storage systems. The search for new materials that could improve the performance of Li-ion batteries (LIBs) is one of today's most challenging tasks. Herein, a stable symmetric LIB based on the bipolar material-MXene/Na0.55Mn1.4Ti0.6O4 was developed. This bipolar hybrid material showed a typical MXene-type layered structure with high conductivity, containing two electrochemically active redox couples, namely, Mn4+/Mn3+ (3.06 V) and Mn2+/Mn (0.25 V). This MXene/Na0.55Mn2O4-based symmetric full cell exhibited the highest energy density of 393.4 W h kg−1 among all symmetric full cells reported so far, wherein it is bestowed with a high average voltage of 2.81 V and a reversible capacity of 140 mA h g−1 at a current density of 100 mA g−1. In addition, it offers a capacity retention of 79.4% after 200 cycles at a current density of 500 mA g−1. This symmetric lithium ion full battery will stimulate further research on new LIBs using the same active materials with improved safety, lower costs and a long life-span

Comparison of BISAP, Ranson, MCTSI, and APACHE II in Predicting Severity and Prognoses of Hyperlipidemic Acute Pancreatitis in Chinese Patients

In recent years, with the developing of living standard, hyperlipidemia becomes the second major reason of acute pancreatitis. It is important to predict the severity and prognosis at early stage of hyperlipidemic acute pancreatitis (HLAP). We compared the BISAP, Ranson, MCTSI, and APACHE II scoring system in predicting MSAP and SAP, local complications, and mortality of HLAP. A total of 326 diagnosed hyperlipidemic acute pancreatitis patients from August 2006 to July 2015 were studied retrospectively. Our result showed that all four scoring systems can be used to predict the severity, local complications, and mortality of HLAP. Ranson did not have significant advantage in predicting severity and prognosis of HLAP compared to other three scoring systems. APACHE II was the best in predicting severity of HLAP, but it had shortcoming in predicting local complications. MCTSI had outstanding performance in predicting local complications, but it was poor in predicting severity and mortality. BISAP score had high accuracy in assessment of severity, local complications, and mortality of HLAP, but the accuracy still needs to be improved in the future

Correlation of Body Mass Index and Waist-Hip Ratio with Severity and Complications of Hyperlipidemic Acute Pancreatitis in Chinese Patients

Hyperlipidemic acute pancreatitis (HLAP) is characterized by critical condition and high recurrence rate compared with non-HLAP. We conducted this study to investigate the value of body mass index and waist-hip ratio in predicting severity and local complications in HLAP. 96 patients with HLAP were categorized by body mass index and waist-hip ratio, respectively. According to the body mass index, they were divided into 3 groups, including normal weight, overweight, and obesity. According to the waist-hip ratio, they were divided into central obesity group and no central obesity group. The body mass index and waist-hip ratio were compared in severity, local complications, and systematic complications of HLAP, using chi-square test and Monte Carlo simulations. The body mass index and waist-hip ratio were correlated with the severity of acute pancreatitis (MAP, MSAP, and SAP), respiratory failure, and circulatory failure in HLAP (p<0.05), but not correlated with the local complications (walled-off necrosis, pancreatic abscess, and pancreatic pseudocyst), renal failure, and gastrointestinal bleeding.The body mass index and waist-hip ratio are valuable in predicting severity and complication in HLAP. We demonstrated that obese patients had an increased risk of developing more serious condition and more complications in HLAP